Presentation is loading. Please wait.

Presentation is loading. Please wait.

IPCV ‘06 August 21 – September 1 Budapest Jussi Parkkinen Markku Hauta-Kasari Department of Computer Science University of Joensuu, Finland

Similar presentations


Presentation on theme: "IPCV ‘06 August 21 – September 1 Budapest Jussi Parkkinen Markku Hauta-Kasari Department of Computer Science University of Joensuu, Finland"— Presentation transcript:

1 IPCV ‘06 August 21 – September 1 Budapest Jussi Parkkinen Markku Hauta-Kasari Department of Computer Science University of Joensuu, Finland http://spectral.joensuu.fi/

2

3

4 Introduction to spectral color

5 COLOR ANALYSIS

6

7

8

9

10 Eriväristen lehtien spektrejä

11 Spectral images from real and artificial indoor plants Kanae Miyazawa

12 Display characterictics

13 Principle of color detection Light source Colored object Detection system biological artificial

14 Color image formation in human eye

15

16

17

18

19

20

21

22

23 RGB vs. Spectrum Is the spectrum needed? RGB is just a 3D projection of a spectrum RGB can produce nice colors on display, but not correct colors

24

25

26

27

28 Spectral approach to color In spectral approach, color is represented by color signal. This causes the color sensation The signal is part of electromagnetic spectrum - in human color vision the range is 380-780 nm In spectral approach, we are not limited into this human visual range

29 What should be the spectral resolution, i.e. the sampling rate in wavelenths?

30 Spectral dependence on sampling

31 Color dependence on sampling

32 Change of RGB-values due to sampling

33 Can we reproduce a spectrum from the RGB-values?

34 Data 1494 color samples in 200 color images –Munsell colors, Color checker, natural colors, wall paint –cameras: Fuji FinePix and Canon Powershot –illuminants: A and D65

35 Methods 1.Polynomial model (R, G, B, R 2, G 2, B 2,..) 2.Kernel models Evaluation delta E and RMSE (for spectra) –average, std, maximum

36 Spectra with largest delta E polynomial model

37 Some preliminary tests with mobile phones cameras

38

39 Spektrikuvan kanavakuvia

40 Spectral Face Image

41

42

43

44

45 Spectral Image

46 Image Types TYPE SPECTRAL CHANNELS --------------------------------------- Gray-scale Trichromatic Spectral –Hyperspectral Real-time spectral Single Three >3 Numerous

47 MEMORY REQUIREMENTS OF IMAGES Image size256x256 512x512 gray-level image 65 kB 262 kB color (RGB-) image 196 kB 786 kB spectral, 20 nm resol. 1 MB 4 MB spectral, 5 nm resol. 3 MB 15 MB

48 Pixels in color image are vectors What is the order of color? What means the average color? How to compute distance in spectral space? What is the structure of spectral color space?

49 Statistical Analysis of Natural Images

50

51 Munsell system for color representation

52 Spectrum and Hue, Saturation, and Value (a) (b) (c)

53 Motivation for spectral color Not to loose important color information To define optimal color sensors To develop better color vision models To develop novel instruments To develop spectral color classifiers and optical implementations for them

54 What is color? ”Color is more than light” ”Computer cannot describe color correctly” ”Color is a perception (of human beings)” => Color cannot been measured! => Is this fair to animals? In spectral approach, color is represented by color signal. This causes the color sensation In spectral approach, we are not limited into this human visual range


Download ppt "IPCV ‘06 August 21 – September 1 Budapest Jussi Parkkinen Markku Hauta-Kasari Department of Computer Science University of Joensuu, Finland"

Similar presentations


Ads by Google